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This article mainly introduces the docker image release and the use of the method of related knowledge, the content is detailed and easy to understand, the operation is simple and fast, with a certain reference value, I believe that after reading this docker image release and the use of the method article will have a harvest, let's take a look.

1) first install docker2) Image search, download, Launch how to use: > docker search # search all the images NAME DESCRIPTION STARS OFFICIAL AUTOMATED/gene-family gene-family analysis docker image 5 / rnaseq RNA-seq analysis docker image build by omics provided by the lecture hall of Group Studies. 3 / gsds-v2 GSDS 2.0-Gene Structure Display Server 1 / reseq whole genome resequence analysis 1 / biocontainer-base Biocontainers base Image centos7 1 / blast-plus blast+ v2.9.0 0 / isoseq3 isoseq3 v3.3.0 build by 0 / bwa BWA v0.7.17 build by 0 / blastall legacy blastall v2.2.26 0 / sratoolkit SRAtoolkit v2.10.3 and aspera v3.9.9.177872 0 / ampliseq-q2 Amplicon sequencing qiime2 v2020.2 image 0 / ampliseq-q1 Amplicon sequencing qiime1 v1.9.1 image 0 / samtools samtools v1.10 build by 0 / bsaseq NGS Bulk Segregant Analysis image 0 / gwas gwas analysis images 0 > docker pull / ampliseq-q1 # download and expand the child image > docker run-- rm-it-m 4G-- cpus 1-v D:\ qiime1-16s:/work / ampliseq-q1:latest # Launch and enter the image installation software and version introduce OTU analysis related software qiime1 v1.9.1mothur v.1.25.0usearch 10.0.240usearch71 6.1.544vsearch v2.15.0 sequence processing related analysis package flash v2.15.0 sequence combined with bioperl biopythonfastqcmultiqcfastpqiime analysis related package blast-2.2.22blat-36cdhit-3.1muscle-3.8.31rdpclassifier-2.2uclust function annotation related analysis software picrust-1.1.4bugbase number According to the analysis of R packets related to visualization: ggplot2TernaryDESeq2edgeRggtreeveganpheatmap advanced analysis correlation package randomForest machine learning scikit-learn machine learning circos circle diagram drawing Krona species abundance circle map LefSe difference comparative analysis

Note: the above list of packages is only a partial example, and there are many more packages actually installed.

Expand sub-image use case 1. Data preparation

Fastmap.txt files and sequencing data files are placed in the data folder, and species annotation database files such as Greengene silva unite are placed in the database directory. Students who need these test data can follow the official account of the group lecture hall and reply for 16s. After the preparation is completed, the directory structure is as follows:

[root@aefe86d682b1 13:58:49 / work/amplicon_demo] # tree data |-- ERR3975186_1.fastq.gz |-- ERR3975186_2.fastq.gz |-- ERR3975187_1.fastq.gz `--ERR3975187_2.fastq.gz fastmap.txt

Set some environment variables to facilitate subsequent calls:

Dbdir=/work/databaseworkdir=/work/amplicon_demodatadir=$workdir/datafastmap=$workdir/fastmap.txtmkdir / work/tmpexport TMPDIR=/work/tmp # prevent insufficient temporary directory storage # # database# major database address: silva_16S_97_seq=$dbdir/SILVA_132_QIIME_release/rep_set/rep_set_16S_only/97/silva_132_97_16S.fna silva_16S_97_tax=$dbdir/SILVA_ 132_QIIME_release/taxonomy/16S_only/97/taxonomy_7_levels.txt greengene_16S_97_seq=$dbdir/gg_13_8_otus/rep_set/97_otus.fastagreengene_16S_97_tax=$dbdir/gg_13_8_otus/taxonomy/97_otu_taxonomy.txtsilva_18S_97_seq=$dbdir/SILVA_132_QIIME_release/rep_set/rep_set_18S_only/97/silva_132_97_18S.fna silva_18S_97_tax=$dbdir/SILVA_132_ QIIME_release/taxonomy/18S_only/97/taxonomy_7_levels.txt unite_ITS_97_seq=$dbdir/unite_ITS8.2/sh_refs_qiime_ver8_97_04.02.2020.fastaunite_ITS_97_tax=$dbdir/unite_ITS8.2/sh_taxonomy_qiime_ver8_97_04.02.2020.txt2. Merge dual-ended reads,flashcd $workdir # back to the working directory mkdir 1.merge_pefor I in `cat $fastmap | grep-v'#'| cut-f 1` Do echo "RUN CMD: flash $datadir/$ {I} _ 1.fastq.gz $datadir/$ {I} _ 2.fastq.gz\-m 10-x 0.2-p 33-t 1\-o $I-d 1.merge_pe" flash $datadir/$ {I} _ 1.fastq.gz $datadir/$ {I} _ 2.fastq.gz\-m 10-x 0.2-p 33-t 1\-o $I-d 1.merge_pedone3. Perform fastqc quality control on the original data cd $workdir # return to the working directory mkdir 2.fastqc#fastqc to view the data quality distribution and other fastqc-t 2$ workdir/1.merge_pe/*extendedFrags.fastq-o $workdir/2.fastqc# quality control results summary cd $workdir/2.fastqcmultiqc.

4. Data quality control: disconnect the original sequence, primers, delete low-quality reads, etc.

Cd $workdir # returns to the working directory mkdir 3.data_qccd 3.data_qc# uses the fastp tool to remove adapter#--qualified_quality_phred the quality value that a base is qualified. # Default 15 means phred quality > = Q15 is qualified. (int [= 15]) #-- unqualified_percent_limit how many percents of bases are allowed to be unqualified#--n_base_limit if one read's number of N base is > n_base_limit, # then this read/pair is discarded #-- unknown detect_adapter_for_pe connector sequence can be set to automatically identify common connectors # for i in `cat $fastmap | grep-v'#'| cut-f 1` Do echo "RUN CMD: fastp-- thread 1-- qualified_quality_phred 10\-- unqualified_percent_limit 50\-- n_base_limit 10\-- length_required 300\-- trim_front1 29\-- trim_tail1 18\-I $workdir/1.merge_pe/$ {I} .extendedFrags.fastq\-o ${I} .clean _ tags.fq .gz\-- adapter_fasta $workdir/data/illumina_multiplex.fa-h ${I} .html-j ${I} .json "fastp-- thread 1-- qualified_quality_phred 10\-- unqualified_percent_limit 50\-- n_base_limit 10\-- length_required 300\-- trim_front1 29\-- trim_tail1 18\-- I $workdir / 1. Cd _ tags.fq.gz ${I} .extendedFrags.fastq\-o ${I} .clean _ Frags.fastq\-detect_adapter_for_pe-h ${I} .html-j ${I} .jsondone go back to the working directory mkdir 4.remove_chimerascd 4.remove_chimeras# to remove the chimera for i in `Frags.fastq | grep-v'#'| cut-f 1` Do # the same repetitive sequence merges vsearch-- derep_fulllength $workdir/3.data_qc/$ {I} .clean _ tags.fq.gz\-- sizeout-- output ${I} .derep.fa # de-chimera vsearch-- uchime3_deno ${I} .derep.fa\-- sizein-- sizeout\-- nonchimeras ${I} .denovo.nonexisteras.rep.fa # same The sequence is restored to multiple vsearch-- rereplicate ${I} .denovo.nonroomeras.rep.fa-- output ${I} .denovo.nonscientieras.fadone # remove the chimera for i in `cat $fastmap according to the reference sequence | grep-v'#'| cut-f 1` Do vsearch-- uchime_ref ${I} .denovo.nonroomeras.fa\-- db $dbdir/rdp_gold.fa\-- sizein-- sizeout-- fasta_width 0\-- nonchimeras ${I} .ref.nonnormaleras.fadoneqime1 Analysis of pick otu clustering method cd $workdir # returns to the working directory mkdir 5.pick_otu_qiimecd 5.pick_otu_qiime# to merge fasta files, and add the serial number for i in `cat $fastmap | grep-v'#'| cut-f 1` Do rename_fa_id.pl-f $workdir/4.remove_chimeras/$i.ref.nonchimeras.fa\-n $I-out $i.fadone# after merging the fa file into qiime.fasta, delete all single sample fa files cat * fa > qiime.fastarm-f * fa### method 1:pick_de_novo_otus.py### output qiime pick otu parameters More: http://qiime.org/scripts/pick_otus.htmlecho pick_otus:denovo_otu_id_prefix OTU > > otu_params_de_novo.txtecho pick_otus:similarity 0.97 > > otu_params_de_novo.txtecho pick_otus:otu_picking_method uclust > > otu_params_de_novo.txt # sortmerna, mothur, trie, uclust_ref, usearch, usearch_ref, blast, usearch71, usearch71_ref,sumaclust, swarm, prefix_suffix, cdhit Uclust.echo assign_taxonomy:reference_seqs_fp $silva_16S_97_seq > > otu_params_de_novo.txtecho assign_taxonomy:id_to_taxonomy_fp $silva_16S_97_tax > > otu_params_de_novo.txtecho assign_taxonomy:similarity 0.8 > > otu_params_de_novo.txtecho assign_taxonomy:assignment_method uclust > > otu_params_de_novo.txt # rdp, blast,rtax, mothur, uclust, sortmerna if it is ITS/18S data It is recommended that the database be changed to UNITE and the method be changed to blast. Detailed instructions for use Please read the official document: http://qiime.org/scripts/assign_taxonomy.htmlpick_de_novo_otus.py-I qiime.fasta-f-o pick_de_novo_otus-p otu_params_de_novo.txtalpha_diversity Analysis cd $workdir # return to the working directory mkdir 8.alpha_diversitycd 8.alpha_diversity#alpha Diversity Index display biom summarize-table-I $workdir/5.pick_otu_qiime/pick_de_novo_otus/otu_table _ clean_rare.biomecho alpha_diversity:metrics observed_species PD_whole_tree,shannon,chao1,simpson Goods_coverage > alpha_params.txtalpha_rarefaction.py-f-I $workdir/5.pick_otu_qiime/pick_de_novo_otus/otu_table_clean.biom-m $fastmap-o. /-p alpha_params.txt-t $workdir/5.pick_otu_qiime/pick_de_novo_otus/rep_set.tre-- retain_intermediate_files-- min_rare_depth 40-- max_rare_depth 2032-- num_steps 1 diversity index difference ratio Compared with qiime with inspection and drawing compare_alpha_diversity.py\-I alpha_div_collated/chao1.txt\-o alpha_chao1_stats\-m $fastmap\-t nonparametric\-c citycompare_alpha_diversity.py\-I alpha_div_collated/chao1.txt\-o alpha_chao1_stats\-m $fastmap\-t parametric\-c citybeta_diversity analysis cd $workdir # back to the working directory Mkdir 9.beta_diversitycd 9.beta_diversityecho beta_diversity:metrics binary_jaccard Bray_curtis,unweighted_unifrac,weighted_unifrac Binary_euclidean > beta_params.txt#-e set the leveling number beta_diversity_through_plots.py-f-I $workdir/5.pick_otu_qiime/pick_de_novo_otus/otu_table_clean.biom-m $fastmap-o. /-t $workdir/5.pick_otu_qiime/pick_de_novo_otus/rep_set.tre-e 2844-p beta_params.txt#beta diversity adonis test compare_categories.py-- method adonis-I unweighted_unifrac _ dm.txt-m $fastmap-c Treatment-o adonis_out-n 999 the article on "methods for publishing and using docker images" ends here. Thank you for reading! I believe you all have a certain understanding of the knowledge of "methods for publishing and using docker images". If you want to learn more, you are welcome to follow the industry information channel.

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